To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling
Agentic AI architectures augment LLMs with external tools, unlocking strong capabilities but potentially incurring substantial costs. Moreover, tool use is not always beneficial: redundant or low-utility calls can even harm task performance. Effective tool use, therefore, hinges on a core LLM decision: whether to call or not call a tool when performing a task. We introduce a principled framework inspired by decision-making theory to understand tool-use decisions along three key factors: necessity, utility, and affordability. Our analysis combines two complementary lenses: a normative perspective that infers true need and utility for optimal tool calls, and a descriptive perspective that i...
| Authors | Qinyuan Wu, Soumi Das, Mahsa Amani et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 May 2026 |
| Last updated at source | 6 August 2026 |
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